Personal image collections can easily include thousands or tens of thousands of images. As image collections grow, retrieving individual images becomes increasingly difficult. Various image retrieval systems have been deployed in order to address this problem. A familiar paradigm for searching documents is one where the user provides a set of search terms and the system returns a list of documents satisfying those search terms, ranked in order of how well each document satisfies the specified search terms. This paradigm has been applied to searching for images in applications such as Google Image Search and Flickr, as well as in a variety of desktop applications. A limitation of such systems is that each image must somehow be annotated with terms that might be used as search terms in order for searches to return any results. It is very time consuming to manually apply such annotations, although some systems automatically annotate images using a limited number of concepts. Moreover, users can only search using search terms that match the terms used in the annotations; such vocabularies tend to be very limited and constrained. Work in the area of information retrieval and specifically query processing has considered the problem of query expansion, but it is difficult to expand queries using terms appropriate for the consumer imaging domain. Such systems tend to either return too few results to be useful, or too many results for the user to effectively consider.